/external/skia/tools/lua/ |
H A D | gradients.lua | 24 gradients = {} 35 gradients[i] = {} 37 gradients[i].filename = filename 47 gradients[i].boundsWidth = width 48 gradients[i].boundsHeight = height 50 gradients[i].colorCount = g.colorCount 51 gradients[i].type = g.type 52 gradients[i].tile = g.tile 60 gradients[i].isEvenlySpaced = isEvenlySpaced 68 gradients[ [all...] |
H A D | gradients.py | 14 c.execute('''CREATE TABLE IF NOT EXISTS gradients ( 26 c.execute("DELETE FROM gradients"); 29 gradients = [] 31 gradients.append(line.split()); 34 "INSERT INTO gradients VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)", 35 gradients);
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/external/skqp/tools/lua/ |
H A D | gradients.lua | 24 gradients = {} 35 gradients[i] = {} 37 gradients[i].filename = filename 47 gradients[i].boundsWidth = width 48 gradients[i].boundsHeight = height 50 gradients[i].colorCount = g.colorCount 51 gradients[i].type = g.type 52 gradients[i].tile = g.tile 60 gradients[i].isEvenlySpaced = isEvenlySpaced 68 gradients[ [all...] |
H A D | gradients.py | 14 c.execute('''CREATE TABLE IF NOT EXISTS gradients ( 26 c.execute("DELETE FROM gradients"); 29 gradients = [] 31 gradients.append(line.split()); 34 "INSERT INTO gradients VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)", 35 gradients);
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/external/tensorflow/tensorflow/contrib/boosted_trees/python/ops/ |
H A D | stats_accumulator_ops.py | 35 """A resource that allows to accumulate gradients and hessians. 53 gradient_shape: A TensorShape, containing shape of gradients. 92 (stamp_token, num_updates, partition_ids, feature_ids, gradients, 103 saver.BaseSaverBuilder.SaveSpec(gradients, slice_spec, 114 def add(self, stamp_token, partition_ids, feature_ids, gradients, hessians): 116 partition_ids, feature_ids, gradients, hessians = (self._make_summary( 117 partition_ids, feature_ids, gradients, hessians)) 121 [gradients], [hessians]) 125 [gradients], [hessians]) 127 def schedule_add(self, partition_ids, feature_ids, gradients, hessian [all...] |
/external/tensorflow/tensorflow/python/ops/ |
H A D | gradients.py | 15 """Implements the graph generation for computation of gradients.""" 23 from tensorflow.python.ops.gradients_impl import gradients namespace 31 "gradients", # tf.gradients.gradients. 32 "hessians", # tf.gradients.hessians
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H A D | gradients_test.py | 15 """Tests for tensorflow.ops.gradients.""" 41 from tensorflow.python.ops import gradients namespace 148 w_grad = gradients.gradients(h, w)[0] 161 gw = gradients.gradients(c, [w])[0] 170 gw = gradients.gradients(wx, [w], colocate_gradients_with_ops=True)[0] 184 gw1 = gradients.gradients( [all...] |
/external/tensorflow/tensorflow/core/kernels/ |
H A D | relu_op_functor.h | 44 // gradients: gradients backpropagated to the Relu op. 47 // backprops: gradients to backpropagate to the Relu inputs. 48 void operator()(const Device& d, typename TTypes<T>::ConstTensor gradients, argument 55 gradients * (features > static_cast<T>(0)).template cast<T>(); 78 // gradients: gradients backpropagated to the Relu6 op. 80 // backprops: gradients to backpropagate to the Relu6 inputs. 81 void operator()(const Device& d, typename TTypes<T>::ConstTensor gradients, argument 88 backprops.device(d) = gradients * ((feature 119 operator ()(const Device& d, typename TTypes<T>::ConstTensor gradients, typename TTypes<T>::ConstTensor activations, typename TTypes<T>::Tensor backprops) argument 157 operator ()(const Device& d, typename TTypes<T>::ConstTensor gradients, typename TTypes<T>::ConstTensor activations, typename TTypes<T>::Tensor backprops) argument [all...] |
H A D | softsign_op.h | 46 // gradients: gradients backpropagated to the Softsign op. 48 // backprops: gradients to backpropagate to the Softsign inputs. 49 void operator()(const Device& d, typename TTypes<T>::ConstTensor gradients, argument 53 gradients / (features.abs() + features.constant(T(1))).square();
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H A D | softplus_op.h | 62 // gradients: gradients backpropagated to the Softplus op. 64 // backprops: gradients to backpropagate to the Softplus inputs. 65 void operator()(const Device& d, typename TTypes<T>::ConstTensor gradients, 69 gradients / ((-features).exp() + features.constant(T(1)));
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H A D | fake_quant_ops_functor.h | 104 void operator()(const Device& d, ConstFlat<float> gradients, 119 backprops.device(d) = gradients * between_nudged_min_max; 155 void operator()(const Device& d, ConstFlat<float> gradients, 165 backprops_wrt_input.device(d) = gradients; 177 backprops_wrt_input.device(d) = gradients * between_min_max; 182 backprop_wrt_min.device(d) = (gradients * below_min).sum(); 187 backprop_wrt_max.device(d) = (gradients * above_max).sum(); 229 // Already verified: gradients, inputs, backprops_wrt_input are of shape [b, d], 233 void operator()(const Device& d, TTypes<float>::ConstMatrix gradients, 241 const auto gradients_chip = gradients [all...] |
/external/tensorflow/tensorflow/contrib/opt/python/training/ |
H A D | multitask_optimizer_wrapper.py | 51 """Optimizer wrapper making all-zero gradients harmless. 113 """Clips gradients of a multitask loss by their global norm. 125 gradients, variables = six.moves.zip(*gradients_variables) 136 nonzero_gradients = [_replace_nonexisting_grad(g) for g in gradients] 138 gradients, _ = clip_ops.clip_by_global_norm( 139 gradients, clip_norm, use_norm=fixed_global_norm) 140 return list(six.moves.zip(gradients, variables)), fixed_global_norm
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H A D | drop_stale_gradient_optimizer.py | 16 """Wrapper optimizer for checking and dropping stale gradients.""" 39 gradients and compares it with the global step at the time of applying the 40 gradients. If the difference is larger than a threshold, it will drop all 41 the computed gradients. 53 gradients. Must be one of the Optimizer classes. 57 gradients. Defaults to "DropStaleGradient". 79 gradients = [] 80 # Number of stale gradients. 99 gradients.append(grad) 101 gradients [all...] |
/external/tensorflow/tensorflow/contrib/layers/python/layers/ |
H A D | optimizers.py | 52 "gradients", 112 If present, gradients for specified 117 This callable takes a `list` of `(gradients, variables)` `tuple`s and 118 returns the same thing with the gradients modified. 133 gradients will be reported. The complete list of possible values 135 colocate_gradients_with_ops: If True, try colocating gradients with the 154 * `gradients` is empty. 237 # Compute gradients. 238 gradients = opt.compute_gradients( 245 gradients [all...] |
/external/tensorflow/tensorflow/contrib/boosted_trees/lib/learner/batch/ |
H A D | ordinal_split_handler.py | 33 next N steps to accumulate gradients and hessians per bucket. 47 4.1) Get the overall gain computed with gradients and hessians of all 49 4.2) Compute tensors of left and right cumulative sum of gradients, hessians 111 gradient_shape: A TensorShape, containing shape of gradients. 170 gradient_shape: A TensorShape, containing shape of gradients. 198 def update_stats(self, stamp_token, example_partition_ids, gradients, 207 gradients: A dense tensor of gradients. 210 0) as gradients. 225 feature_ids, gradients, hessian [all...] |
H A D | base_split_handler.py | 51 gradient_shape: A TensorShape, containing shape of gradients. 71 def update_stats(self, stamp_token, example_partition_ids, gradients, 80 gradients: A dense tensor of gradients. 83 0) as gradients. 96 def update_stats_sync(self, stamp_token, example_partition_ids, gradients, 105 gradients: A dense tensor of gradients. 108 0) as gradients. 122 stamp_token, example_partition_ids, gradients, hessian [all...] |
H A D | categorical_split_handler.py | 60 gradient_shape: A TensorShape, containing shape of gradients. 84 def update_stats(self, stamp_token, example_partition_ids, gradients, 93 gradients: A dense tensor of gradients. 96 0) as gradients. 124 filtered_gradients = array_ops.gather(gradients, example_indices) 132 # The bias is computed on gradients and hessians (and not 136 gradients, mapped_partitions, array_ops.size(unique_partitions)) 172 # Get the aggregated gradients and hessians per <partition_id, feature_id> 174 num_minibatches, partition_ids, feature_ids, gradients, hessian [all...] |
H A D | ordinal_split_handler_test.py | 59 gradients = array_ops.constant([0.2, -0.5, 1.2, 4.0]) 88 gradients, 100 gradients, 184 # Batch size is 4, 2 gradients per each instance. 185 gradients = array_ops.constant( 223 gradients, 235 gradients, 270 # Batch size is 4, 2 gradients per each instance. 271 gradients = array_ops.constant( 309 gradients, [all...] |
/external/tensorflow/tensorflow/contrib/nn/python/ops/ |
H A D | fwd_gradients.py | 24 from tensorflow.python.ops.gradients_impl import gradients namespace 31 gradients. There is additional overhead on graph construction, but runtime 53 # generated by the first tf.gradients call. 57 dydxs = gradients(ys, xs, grad_ys=us) 59 # deal with strange types that tf.gradients returns but can't deal with 74 dysdx = gradients(dydxs, us, grad_ys=grad_xs)
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/external/tensorflow/tensorflow/contrib/boosted_trees/python/kernel_tests/ |
H A D | split_handler_ops_test.py | 43 gradients = array_ops.constant([2.4, -0.6, 8.0]) 51 gradients=gradients, 115 gradients = array_ops.constant([[2.4, 3.0], [-0.6, 0.1], [8.0, 1.0]]) 124 gradients=gradients, 155 gradients = array_ops.constant([]) 163 gradients=gradients, 195 gradients [all...] |
H A D | stats_accumulator_ops_test.py | 29 """Tests for scalar gradients and hessians accumulator.""" 42 gradients=[0.1, 0.3], 70 gradients=[0.1, 0.3, 0.8], 99 gradients=[0.1, 0.3], 105 gradients=[0.1], 131 gradients=[0.1, 0.3], 173 gradients=[0.1, 0.3], 183 gradients=[0.4, 0.5], 207 gradients=[0.1, 0.3, 0.1], 218 """Tests for tensor gradients an [all...] |
/external/tensorflow/tensorflow/contrib/rnn/python/kernel_tests/ |
H A D | fused_rnn_cell_test.py | 61 basic_grads = sess.run(gradients_impl.gradients(packed_outputs, inputs)) 63 gradients_impl.gradients(packed_outputs, basic_vars)) 76 fused_static_grads = sess.run(gradients_impl.gradients(outputs, inputs)) 78 gradients_impl.gradients(outputs, fused_static_vars)) 98 gradients_impl.gradients(outputs, inputs)) 100 gradients_impl.gradients(outputs, fused_dynamic_vars)) 133 basic_grads = sess.run(gradients_impl.gradients(packed_outputs, inputs)) 135 gradients_impl.gradients(packed_outputs, basic_vars)) 154 fused_grads = sess.run(gradients_impl.gradients(outputs, inputs)) 155 fused_wgrads = sess.run(gradients_impl.gradients(output [all...] |
/external/tensorflow/tensorflow/contrib/batching/python/ops/ |
H A D | batch_ops.py | 32 gradients = [] 34 gradients.append( 41 return gradients
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/external/tensorflow/tensorflow/contrib/compiler/ |
H A D | jit_test.py | 28 from tensorflow.python.ops import gradients namespace 182 x_grads = gradients.gradients([y_c], [x])[0] 185 op for op in operations if "gradients/compiled" in op.name] 187 op for op in operations if "gradients/not_compiled" in op.name] 212 grad_a1 = gradients.gradients(a1t, a1, name="GA")[0] 213 grad_a2 = gradients.gradients(a2t, a2, name="GB")[0] 234 grad_a1 = gradients [all...] |
/external/tensorflow/tensorflow/contrib/cudnn_rnn/python/kernel_tests/ |
H A D | cudnn_rnn_ops_benchmark.py | 113 all_grads = gradients_impl.gradients( 138 gradients = gradients_impl.gradients([outputs, final_state], 140 training_op = control_flow_ops.group(*gradients) 162 gradients = gradients_impl.gradients([outputs, final_state], 164 training_op = control_flow_ops.group(*gradients)
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